U.S. patent number 10,528,714 [Application Number 15/867,147] was granted by the patent office on 2020-01-07 for method and apparatus for authenticating user using electrocardiogram signal.
This patent grant is currently assigned to Samsung Electronics Co., Ltd.. The grantee listed for this patent is Samsung Electronics Co., Ltd.. Invention is credited to Chisung Bae, Sang Joon Kim, Haixiao Liu, Yang Liu, Chao Zhang.
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United States Patent |
10,528,714 |
Zhang , et al. |
January 7, 2020 |
Method and apparatus for authenticating user using
electrocardiogram signal
Abstract
A method and apparatus to authenticate a registered user are
described. The method and apparatus include a processor configured
to identify a first electrocardiogram (ECG) signal measured from
the user, and determine a similarity between the first ECG signal
and a second ECG signal based on the identified first ECG signal
and the second ECG signal included in a reference ECG signal set.
The processor is also configured to determine an authentication
threshold corresponding to the reference ECG signal set, and
determine whether to authenticate the first ECG signal measured
from the user by comparing the determined similarity and the
authentication threshold.
Inventors: |
Zhang; Chao (Beijing,
CN), Liu; Haixiao (Beijing, CN), Liu;
Yang (Beijing, CN), Bae; Chisung (Yongin-si,
KR), Kim; Sang Joon (Hwaseong-si, KR) |
Applicant: |
Name |
City |
State |
Country |
Type |
Samsung Electronics Co., Ltd. |
Suwon-si |
N/A |
KR |
|
|
Assignee: |
Samsung Electronics Co., Ltd.
(Suwon-si, KR)
|
Family
ID: |
62783062 |
Appl.
No.: |
15/867,147 |
Filed: |
January 10, 2018 |
Prior Publication Data
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|
|
Document
Identifier |
Publication Date |
|
US 20180196932 A1 |
Jul 12, 2018 |
|
Foreign Application Priority Data
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|
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Jan 11, 2017 [CN] |
|
|
2017 1 0019864 |
Nov 10, 2017 [KR] |
|
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10-2017-0149410 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B
5/7264 (20130101); G06K 9/6271 (20130101); G06F
21/32 (20130101); A61B 5/0452 (20130101); G06K
9/4628 (20130101); G06K 9/00536 (20130101); A61B
5/04023 (20130101); A61B 5/117 (20130101); G06K
2009/00939 (20130101); A61B 5/04012 (20130101); G16H
50/20 (20180101) |
Current International
Class: |
G05B
19/00 (20060101); A61B 5/00 (20060101); A61B
5/0452 (20060101); G06F 21/32 (20130101); G05B
23/00 (20060101); G06F 7/00 (20060101); G06F
7/04 (20060101); G08B 29/00 (20060101); G08C
19/00 (20060101); H04B 1/00 (20060101); H04B
3/00 (20060101); H04Q 9/00 (20060101); A61B
5/04 (20060101); A61B 5/117 (20160101); G06K
9/00 (20060101) |
Field of
Search: |
;340/5.82 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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|
|
|
|
|
|
10-1570679 |
|
Nov 2015 |
|
KR |
|
10-1587874 |
|
Jan 2016 |
|
KR |
|
10-2016-0088047 |
|
Jul 2016 |
|
KR |
|
10-1646566 |
|
Aug 2016 |
|
KR |
|
10-1657005 |
|
Sep 2016 |
|
KR |
|
Primary Examiner: Shah; Tanmay K
Attorney, Agent or Firm: NSIP Law
Claims
What is claimed is:
1. A method of authenticating a user, comprising: identifying a
first electrocardiogram (ECG) signal measured from the user;
determining a similarity between the first ECG signal and a second
ECG signal based on the identified first ECG signal and the second
ECG signal included in a reference ECG signal set; determining an
authentication threshold, based on a state of the user,
corresponding to the reference ECG signal set; and determining
whether to authenticate the first ECG signal measured from the user
by comparing the determined similarity and the authentication
threshold, whether the reference ECG signal set is updated using
the first ECG signal, when the authentication with respect to the
first ECG signal is a success.
2. The method of claim 1, wherein the determining of the
authentication threshold comprises: determining an authentication
threshold model corresponding to the reference ECG signal set; and
acquiring the authentication threshold by applying a feature vector
of the reference ECG signal set to the determined authentication
threshold model.
3. The method of claim 2, wherein the feature vector is extracted
from the preprocessed second ECG signal through a neural
network.
4. The method of claim 2, wherein the authentication threshold has
a positive correlation with any one or any combination of a number
of second ECG signals included in the reference ECG signal set.
5. The method of claim 1, wherein the determining of whether to
authenticate comprises determining that an authentication is a
success in response to a maximum similarity among the one or more
similarities being greater than the authentication threshold, or
determining that the authentication is a failure in response to the
maximum similarity being less than or equal to the authentication
threshold.
6. The method of claim 5, further comprising: updating the
reference ECG signal set in response to the authentication being
determined as the success, wherein the updating comprises updating
the reference ECG signal set using the first ECG signal in response
to the maximum similarity being greater than an update threshold of
the reference ECG signal set.
7. A method of authenticating a user, comprising: identifying a
first electrocardiogram (ECG) signal measured from the user;
determining a similarity between the first ECG signal and a second
ECG signal based on the identified first ECG signal and the second
ECG signal included in a reference ECG signal set; determining an
authentication threshold, based on a state of the user,
corresponding to the reference ECG signal set; determining whether
to authenticate the first ECG signal measured from the user by
comparing the determined similarity and the authentication
threshold, wherein the determining of whether to authenticate
comprises determining that an authentication is a success in
response to a maximum similarity among the one or more similarities
being greater than the authentication threshold, or determining
that the authentication is a failure in response to the maximum
similarity being less than or equal to the authentication
threshold; and updating the reference ECG signal set in response to
the authentication being determined as the failure, wherein the
updating comprises updating the reference ECG signal set using the
first ECG signal in response to the user being authenticated using
an authentication method excluding an ECG signal-based user
authentication.
8. The method of claim 7, wherein the authentication of the user by
the authentication method excluding the ECG signal-based user
authentication is performed in response to a number of updates
being less than a preset threshold.
9. The method of claim 6, wherein the updating of the reference ECG
signal set using the first ECG signal comprises adding the
authenticated first ECG signal to the reference ECG signal set as
the second ECG signal in response to a number of second ECG signals
included in the reference ECG signal set being less than a preset
number, or coupling the measured first ECG signal and the second
ECG signal indicating the maximum similarity in response to the
number of second ECG signals included in the reference ECG signal
set being greater than or equal to the preset number.
10. The method of claim 7, wherein the updating of the reference
ECG signal set using the first ECG signal comprises adding the
unauthenticated first ECG signal to the reference ECG signal set as
the second ECG signal in response to a number of second ECG signals
included in the reference ECG signal set being less than a preset
number, or adding the first ECG signal to the reference ECG signal
set instead of using the second ECG signal corresponding to a
maximum similarity sum between the second ECG signal and other
second ECG signals included in the reference ECG signal set in
response to the number of second ECG signals included in the
reference ECG signal set being greater than or equal to the preset
number.
11. The method of claim 9, wherein the coupling comprises coupling
the first ECG signal and the second ECG signal based on a weight of
the first ECG signal and a weight of the second ECG signal having a
maximum similarity with the first ECG signal in the reference ECG
signal set.
12. A non-transitory computer-readable storage medium storing
instructions that, when executed by a processor, cause the
processor to perform the method of claim 1.
13. An apparatus to authenticate a user, comprising: a processor;
and a memory configured to store one or more instructions
executable by the processor, wherein, when the one or more
instructions is executed by the processor, the processor is
configured to identify a first electrocardiogram (ECG) signal
measured from the user, determine a similarity between the first
ECG signal and a second ECG signal based on the identified first
ECG signal and the second ECG signal included in a reference ECG
signal set, determine an authentication threshold, based on a state
of the user, corresponding to the reference ECG signal set, and
determine whether to authenticate the first ECG signal measured
from the user by comparing the determined similarity and the
authentication threshold, whether the reference ECG signal set is
updated using the first ECG signal, when the authentication with
respect to the first ECG signal is a success.
14. The apparatus of claim 13, wherein the processor is configured
to determine an authentication threshold model corresponding to the
reference ECG signal set, and to acquire the authentication
threshold by applying a feature vector of the reference ECG signal
set to the determined authentication threshold model.
15. The apparatus of claim 14, wherein the authentication threshold
has a positive correlation with any one or any combination of a
number of second ECG signals included in the reference ECG signal
set.
16. The apparatus of claim 13, wherein the processor is configured
to determine that an authentication is a success in response to a
maximum similarity among the one or more similarities being greater
than the authentication threshold, or to determine that the
authentication is a failure in response to the maximum similarity
being less than or equal to the authentication threshold.
17. The apparatus of claim 16, wherein the processor is configured
to update the reference ECG signal set in response to the
authentication being determined as the success, and to update the
reference ECG signal set using the first ECG signal in response to
the maximum similarity being greater than an update threshold of
the reference ECG signal set.
18. The apparatus of claim 16, wherein the processor is configured
to update the reference ECG signal set in response to the
authentication being determined as the failure, and to update the
reference ECG signal set using the first ECG signal in response to
the user being authenticated using an authentication method
excluding an ECG signal-based user authentication.
19. The apparatus of claim 17, wherein, in the case of updating the
reference ECG signal set using the first ECG signal, the processor
is configured to add the authenticated first ECG signal to the
reference ECG signal set as the second ECG signal in response to a
number of second ECG signals included in the reference ECG signal
set being less than a preset number, or couple the measured first
ECG signal and the second ECG signal indicating the maximum
similarity in response to the number of second ECG signals included
in the reference ECG signal set being greater than or equal to the
preset number.
20. The apparatus of claim 18, wherein, in the case of updating the
reference ECG signal set using the first ECG signal, the processor
is configured to add the unauthenticated first ECG signal to the
reference ECG signal set as the second ECG signal in response to a
number of second ECG signals included in the reference ECG signal
set being less than a preset number, or to add the first ECG signal
to the reference ECG signal set instead of using the second ECG
signal corresponding to a maximum similarity sum between the second
ECG signal and other second ECG signals included in the reference
ECG signal set in response to the number of second ECG signals
included in the reference ECG signal set being greater than or
equal to the preset number.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit under 35 USC .sctn. 119(a) of
Chinese patent Application No. 201710019864.1 filed on Jan. 11,
2017 in the State of Intellectual Property Office of the People's
Republic of China and Korean Patent Application No. 10-2017-0149410
filed on Nov. 10, 2017 in the Korean Intellectual Property Office,
the entire disclosures of which are incorporated herein by
reference for all purposes.
BACKGROUND
1. Field
The following description relates to a method and apparatus to
authenticate a user using an electrocardiogram (ECG) signal.
2. Description of Related Art
Authentication technology using biometric recognition identifies a
user based on inherent biological characteristics or behavioral
characteristics of an individual, for example, an iris, a
fingerprint, voice, a vein pattern, and a gait. Biometric
characteristics used to authenticate differ for each person, but
remain on average consistent through the person's lifetime.
Biometric recognition technology represents a technique to
authenticate the person by extracting a signal or data associated
with a biometric characteristic of the person and comparing the
extracted signal or data to previously stored data.
Further, authenticating using biometric characteristics, such as
electrocardiogram (ECG) signal-based user authentication, are not
easily falsified and have a relatively high stability and high
identification rates. Accordingly, the ECG signal-based user
authentication research is ongoing.
SUMMARY
This Summary is provided to introduce a selection of concepts in a
simplified form that are further described below in the Detailed
Description. This Summary is not intended to identify key features
or essential features of the claimed subject matter, nor is it
intended to be used as an aid in determining the scope of the
claimed subject matter.
In accordance with an example, there may be provided a method of
authenticating a user, including: identifying a first
electrocardiogram (ECG) signal measured from the user; determining
a similarity between the first ECG signal and a second ECG signal
based on the identified first ECG signal and the second ECG signal
included in a reference ECG signal set; determining an
authentication threshold corresponding to the reference ECG signal
set; and determining whether to authenticate the first ECG signal
measured from the user by comparing the determined similarity and
the authentication threshold.
The determining of the authentication threshold may include:
determining an authentication threshold model corresponding to the
reference ECG signal set; and acquiring the authentication
threshold by applying a feature vector of the reference ECG signal
set to the determined authentication threshold model.
The feature vector may be extracted from the preprocessed second
ECG signal through a neural network.
The authentication threshold has a positive correlation with any
one or any combination of a number of second ECG signals included
in the reference ECG signal set.
The determining of whether to authenticate may include determining
that an authentication may be a success in response to a maximum
similarity among the one or more similarities being greater than
the authentication threshold, or determining that the
authentication may be a failure in response to the maximum
similarity being less than or equal to the authentication
threshold.
The method further including: updating the reference ECG signal set
in response to the authentication being determined as the success,
and wherein the updating may include updating the reference ECG
signal set using the first ECG signal in response to the maximum
similarity being greater than an update threshold of the reference
ECG signal set.
The method further including: updating the reference ECG signal set
in response to the authentication being determined as the failure,
wherein the updating may include updating the reference ECG signal
set using the first ECG signal in response to the user being
authenticated using an authentication method excluding an ECG
signal-based user authentication.
The authentication of the user by the authentication method
excluding the ECG signal-based user authentication may be performed
in response to a number of updates being less than a preset
threshold.
The updating of the reference ECG signal set using the first ECG
signal may include adding the authenticated first ECG signal to the
reference ECG signal set as the second ECG signal in response to a
number of second ECG signals included in the reference ECG signal
set being less than a preset number, or coupling the measured first
ECG signal and the second ECG signal indicating the maximum
similarity in response to the number of second ECG signals included
in the reference ECG signal set being greater than or equal to the
preset number.
The updating of the reference ECG signal set using the first ECG
signal may include adding the unauthenticated first ECG signal to
the reference ECG signal set as the second ECG signal in response
to a number of second ECG signals included in the reference ECG
signal set being less than a preset number, or adding the first ECG
signal to the reference ECG signal set instead of using the second
ECG signal corresponding to a maximum similarity sum between the
second ECG signal and other second ECG signals included in the
reference ECG signal set in response to the number of second ECG
signals included in the reference ECG signal set being greater than
or equal to the preset number.
The coupling may include coupling the first ECG signal and the
second ECG signal based on a weight of the first ECG signal and a
weight of the second ECG signal having a maximum similarity with
the first ECG signal in the reference ECG signal set.
In accordance with an example, there may be provided a
non-transitory computer-readable storage medium storing
instructions that, when executed by a processor, cause the
processor to perform the method described above.
In accordance with an example, there may be provided an apparatus
to authenticate a user, including: a processor configured to:
identify a first electrocardiogram (ECG) signal measured from the
user, determine a similarity between the first ECG signal and a
second ECG signal based on the identified first ECG signal and the
second ECG signal included in a reference ECG signal set, determine
an authentication threshold corresponding to the reference ECG
signal set, and determine whether to authenticate the first ECG
signal measured from the user by comparing the determined
similarity and the authentication threshold.
The data encoding apparatus may also include a memory configured to
store instructions,
wherein the processor may be further configured to execute the
instructions to configure the processor to identify the first ECG
signal measured from the user, determine the similarity between the
first ECG signal and the second ECG signal based on the identified
first ECG signal and the second ECG signal included in the
reference ECG signal set, determine the authentication threshold
corresponding to the reference ECG signal set, and determine
whether to authenticate the first ECG signal measured from the user
by comparing the determined similarity and the authentication
threshold.
The processor may be configured to determine an authentication
threshold model corresponding to the reference ECG signal set, and
to acquire the authentication threshold by applying a feature
vector of the reference ECG signal set to the determined
authentication threshold model.
The authentication threshold has a positive correlation with any
one or any combination of a number of second ECG signals included
in the reference ECG signal set.
The processor may be configured to determine that an authentication
may be a success in response to a maximum similarity among the one
or more similarities being greater than the authentication
threshold, or to determine that the authentication may be a failure
in response to the maximum similarity being less than or equal to
the authentication threshold.
The processor may be configured to update the reference ECG signal
set in response to the authentication being determined as the
success, and to update the reference ECG signal set using the first
ECG signal in response to the maximum similarity being greater than
an update threshold of the reference ECG signal set.
The processor may be configured to update the reference ECG signal
set in response to the authentication being determined as the
failure, and to update the reference ECG signal set using the first
ECG signal in response to the user being authenticated using an
authentication method excluding an ECG signal-based user
authentication.
In the case of updating the reference ECG signal set using the
first ECG signal, the processor may be configured to add the
authenticated first ECG signal to the reference ECG signal set as
the second ECG signal in response to a number of second ECG signals
included in the reference ECG signal set being less than a preset
number, or couple the measured first ECG signal and the second ECG
signal indicating the maximum similarity in response to the number
of second ECG signals included in the reference ECG signal set
being greater than or equal to the preset number.
In the case of updating the reference ECG signal set using the
first ECG signal, the processor may be configured to add the
unauthenticated first ECG signal to the reference ECG signal set as
the second ECG signal in response to a number of second ECG signals
included in the reference ECG signal set being less than a preset
number, or to add the first ECG signal to the reference ECG signal
set instead of using the second ECG signal corresponding to a
maximum similarity sum between the second ECG signal and other
second ECG signals included in the reference ECG signal set in
response to the number of second ECG signals included in the
reference ECG signal set being greater than or equal to the preset
number.
Other features and aspects will be apparent from the following
detailed description, the drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a diagram illustrating an example of authenticating a
user using an electrocardiogram (ECG) signal.
FIGS. 2A and 2B are graphs showing examples of an ECG signal of the
same user in a different state.
FIG. 3 is a flowchart illustrating an example of a user
authentication method performed by a user authentication apparatus
using an ECG signal.
FIG. 4 is a flowchart illustrating an example of a method of
updating a reference ECG signal set using a user authentication
apparatus.
FIG. 5 is a flowchart illustrating an example of a method of
determining an authentication threshold using an authentication
threshold model.
FIG. 6 is a diagram illustrating an example of a user
authentication apparatus including a processor and a memory to
perform user authentication.
FIG. 7 illustrates an example of a test result acquired by applying
an ECG signal to user authentication.
Throughout the drawings and the detailed description, the same
reference numerals refer to the same elements. The drawings may not
be to scale, and the relative size, proportions, and depiction of
elements in the drawings may be exaggerated for clarity,
illustration, and convenience.
DETAILED DESCRIPTION
The following detailed description is provided to assist the reader
in gaining a comprehensive understanding of the methods,
apparatuses, and/or systems described herein. However, various
changes, modifications, and equivalents of the methods,
apparatuses, and/or systems described herein will be apparent after
an understanding of the disclosure of this application. For
example, the sequences of operations described herein are merely
examples, and are not limited to those set forth herein, but may be
changed as will be apparent after an understanding of the
disclosure of this application, with the exception of operations
necessarily occurring in a certain order. Also, descriptions of
features that are known in the art may be omitted for increased
clarity and conciseness.
The following structural or functional descriptions are exemplary
to merely describe the examples, and the scope of the examples is
not limited to the descriptions provided in the present
specification. Various changes and modifications can be made
thereto by those of ordinary skill in the art.
Although terms of "first" or "second" are used to explain various
components, the components are not limited to the terms. These
terms should be used only to distinguish one component from another
component. For example, a "first" component may be referred to as a
"second" component, or likewise, and the "second" component may be
referred to as the "first" component within the scope of the right
according to the concept of the present disclosure.
Throughout the specification, when an element, such as a layer,
region, or substrate, is described as being "on," "connected to,"
or "coupled to" another element, it may be directly "on,"
"connected to," or "coupled to" the other element, or there may be
one or more other elements intervening therebetween. In contrast,
when an element is described as being "directly on," "directly
connected to," or "directly coupled to" another element, there can
be no other elements intervening therebetween.
As used herein, the singular forms are intended to include the
plural forms as well, unless the context clearly indicates
otherwise. It should be further understood that the terms
"comprises/includes" and/or "comprising/including," when used in
this specification, specify the presence of stated features,
integers, steps, operations, elements, components or a combination
thereof, but do not preclude the presence or addition of one or
more other features, integers, steps, operations, elements,
components, and/or groups thereof.
Unless otherwise defined herein, all terms used herein including
technical or scientific terms have the same meanings as those
generally understood by one of ordinary skill in the art. Terms
defined in dictionaries generally used should be construed to have
meanings matching with contextual meanings in the related art and
are not to be construed as an ideal or excessively formal meaning
unless otherwise defined herein.
The following examples may be used to recognize a fingerprint of a
user. For instance, an operation of recognizing a fingerprint of a
user is used to conduct an operation of authenticating or
identifying the user. The operation of authenticating the user
includes an operation of determining whether the user is a
registered user. In this example, a result of the operation of
authenticating the user is output to be true or false. The
operation of identifying the user may include, for example, an
operation of determining to which registered user the user
corresponds among a plurality of registered users. In this example,
a result is output of the operation of identifying the user as an
identifier (ID) of any one registered user. If the user corresponds
to none of the registered users, a signal is output indicating that
the user is not identified.
The examples may be embodied using various hardware products, for
example, a personal computer (PC), a laptop computer, a tablet
computer, a smartphone, a television (TV), a smart electronic
device, a smart vehicle, a kiosk, a wearable device, and the like.
For example, the examples may be applied to authenticating a user
in a smartphone, a mobile device, a smart home system, and the
like. The examples may be applied to a payment service through a
user authentication. Also, the examples may be applied to a smart
vehicle system that automatically starts a vehicle by
authenticating a user. Hereinafter, the examples will be described
with reference to the accompanying drawings, and like reference
numerals in the drawings refer to like elements throughout.
FIG. 1 is a diagram illustrating an example of authenticating a
user using an electrocardiogram (ECG) signal.
FIG. 1 illustrates a user authentication apparatus 100 and a
reference ECG signal set 110. The user authentication apparatus 100
refers to an apparatus that performs authentication of an identity
of a user (hereinafter, also referred to as user authentication)
using an ECG signal. For example, the user authentication apparatus
100 measures an ECG signal and performs user authentication.
Hereinafter, a first ECG signal is used to distinguish the ECG
signal of the user measured by the user authentication apparatus
100 from another ECG signal.
In one example, the user authentication apparatus 100 may be
embedded in, for example, a mobile phone, a cellular phone, a
smartphone, a personal computer (PC), a laptop computer, a
notebook, a netbook, a tablet, a personal digital assistant (PDA),
a digital camera, a game console, an MP3 player, a personal
multimedia player (PMP), an electronic book (E-book), a navigation,
a disk player, a set-top box, a home electronic appliance, a
communication device, a display device, or another electronic
device, or may interwork therewith. Also, a voice recognition
apparatus may be embedded in, for example, a smart electronic
device, a smart vehicle, an autonomous driving device, a smart home
environment, a smart building environment, a smart office
environment, or a smart electronic security system, or may
interwork therewith. The user authentication apparatus 100 may be
included in a wearable device that may be provided to a body of the
user and may operate, or may interwork with the wearable device.
The wearable device may be in a form of, for example, a ring, a
watch, glasses, a bracelet, a belt, a band, a necklace, an ear
ring, a helmet, or clothing.
The reference ECG signal set 110 includes one or more ECG signals
used to authenticate the user using the first ECG signal measured
by the user authentication apparatus 100. A reference ECG signal
indicates an ECG signal used when the user authentication apparatus
100 performs the user authentication. Hereinafter, the reference
ECG signal is referred to as a second ECG signal to be
distinguished from another ECG signal.
The second ECG signal is an ECG signal of the user in each of
various states. For example, the second ECG signal represents an
ECG signal initially registered by the user, an ECG signal when the
user is exercising, an ECG signal when the user is in a tranquil
state, an ECG signal when the user is in an excited state, an ECG
signal when the user is in an alcohol-drunk state. The ECG signal
is updated several times using an authenticated ECG signal after
predetermined time intervals.
The second ECG signal of the reference ECG signal set is stored as
one of the ECG signal, in a form of a feature vector of the second
ECG signal, in a form of a key point extracted from the second ECG
signal, and in other suitable forms.
For example, the user authentication apparatus 100 extracts a
feature vector by processing an ECG signal measured from the user
to use as the second ECG signal. The user authentication apparatus
100 executes processing of the ECG signal, for example, filtering,
a key point extraction, and segmentation of the ECG signal. The
user authentication apparatus 100 extracts the feature vector
using, for example, a trained neural network.
The user authentication apparatus 100 identifies the first ECG
signal measured from the user to be authenticated. The user
authentication apparatus 100 identifies the first ECG signal using
an embedded sensor, or identifies the first ECG signal using an
external sensor thereto or other devices. The external sensor to
the user authentication apparatus 100 communicates wire or
wirelessly with the user authentication apparatus 100 to transmit
the first ECG signal. In one example, the user authentication
apparatus 100 periodically requests the first ECG signal or, once
the external sensor detects the first ECG signal, the external
sensor automatically transmits the first ECG signal to the user
authentication apparatus 100. The user authentication apparatus 100
identifies the first ECG signal measured from the user using other
appropriate schemes.
The user authentication apparatus 100 identifies the first ECG
signal measured from the user in response to a process or a
manipulation that requires user authentication. For example, the
process that requires the user authentication includes a screen
unlock, a payment progress, and opening of an encrypted file.
The user authentication apparatus 100 determines whether to
authenticate the user by comparing the first ECG signal and one or
more second ECG signals included in the reference ECG signal set
110. For example, the user authentication apparatus 100 determines
a similarity between the first ECG signal and each of one or more
second ECG signals included in the ECG signal set 110, and
determines whether to authenticate the first ECG signal measured
from the user by comparing a maximum similarity among the
determined similarities and an authentication threshold used for
the user authentication.
The reference ECG signal set 110 is updated based on whether the
authentication of the first ECG signal is a success or a failure,
that is, whether the first ECG signal is successfully
authenticated. For example, in response to the first ECG signal
being successfully authenticated, the reference ECG signal set 110
is updated by adding the authenticated first ECG signal to the
reference ECG signal set 110 or by coupling the authenticated first
ECG signal and the existing second ECG signal. As another example,
in response to the user being authenticated using another process
regardless of a failure in authenticating the first ECG signal, the
reference ECG signal set 110 is updated by adding the
unauthenticated first ECG signal to the reference ECG signal set
110 or by replacing the existing second ECG signal with the
unauthenticated first ECG signal.
The user authentication apparatus 100 accumulatively updates the
reference ECG signal set 110 to reflect various states of the user.
Accordingly, the accuracy of authentication performed by the user
authentication apparatus 100 is enhanced.
FIGS. 2A and 2B are graphs 210 and 220 showing examples of an ECG
signal of the same user in a different state.
Compared to a facial recognition, a fingerprint recognition, and a
voice recognition, it is relatively difficult to forge an ECG
signal. However, even an ECG signal acquired from the same user may
vary based on a state of the user. For example, an ECG signal
acquired from the same user may show a great difference in various
states. For example, an ECG signal before or after exercise, an ECG
signal before or after a meal, and an ECG signal before or after a
mood variation may be slightly different or significantly
different.
The graph 210 of FIG. 2A relates to a first ECG signal and the
graph 220 of FIG. 2B relates to a second ECG signal. For example,
the second ECG signal is an existing ECG signal registered from the
user and the first ECG signal is an ECG signal measured from the
user by the user authentication apparatus 100 of FIG. 1.
A reference ECG signal set includes one or more second ECG signals.
For example, the reference ECG signal set includes a single second
ECG signal that is an ECG signal initially registered by the user,
and includes a plurality of second ECG signals by registering and
updating another second ECG signal.
Referring to the graphs 210 and 220 FIGS. 2A and 2B, even in the
case of being the same user, the first ECG signal and the second
ECG signal may have a relatively small similarity therebetween
because the first ECG signal and the second ECG signal are ECG
signals acquired in different physiological states or different
mental states. Accordingly, the user authentication apparatus 100
compares the first ECG signal and the second ECG signal and
recognizes the first ECG signal and the second ECG signal as ECG
signals acquired from different users.
The user authentication apparatus 100 updates the reference ECG
signal set by performing ECG signal authentication a number of
times. Accordingly, the reference ECG signal set includes one or
more second ECG signals. The user authentication apparatus 100
determines a similarity between the first ECG signal and each of
one or more second ECG signals. In an example, the user
authentication apparatus 100 determines that the authentication is
a success in response to a maximum similarity among the
similarities being greater than a preset or predefined
authentication threshold, or determines that the authentication is
a failure in response to the maximum similarity being less than or
equal to the authentication threshold.
The authentication threshold, as a threshold of a similarity for
successfully authenticating the first ECG signal, is, for example,
a preset value or a value that is determined by the user
authentication apparatus 100 based on the reference ECG signal
set.
FIG. 3 is a flowchart illustrating an example of a user
authentication method using an ECG signal that is performed by a
user authentication apparatus, such as the user authentication
apparatus 100 of FIG. 1. Referring to FIGS. 1 and 3, in operation
310, the user authentication apparatus 100 identifies a first ECG
signal measured from a user to be authenticated. For example, the
user authentication apparatus 100 identifies the measured first ECG
signal using an embedded sensor or another device.
In operation 320, the user authentication apparatus 100 determines
a similarity between the first ECG signal and each of one or more
second ECG signals based on the identified first ECG signal and the
one or more second ECG signals included in the reference ECG signal
set 110, which is a comparison target set.
In an example, the reference ECG signal set 110 includes one or
more second ECG signals. For example, the reference ECG signal set
110 includes a second ECG signal initially registered by the user,
includes another second ECG signal additionally registered by the
user, or includes an updated second ECG signal.
The reference ECG signal set 110 stores the second ECG signal in a
form of at least one of an ECG signal, a feature vector of the ECG
signal, a key point extracted from the ECG signal, or in other
similar forms.
The user authentication apparatus 100 determines a similarity
between the first ECG signal measured from the user and the second
ECG signal included in the reference ECG signal set 110. In an
example, the similarity with the first ECG signal is determined
based on a form in which the second ECG signal is stored.
For example, the user authentication apparatus 100 extracts a
feature vector of the first ECG signal and compares a similarity
between the extracted feature vector of the first ECG signal and
the second ECG signal stored in the form of the feature vector. The
user authentication apparatus 100 executes processing, for example,
filtering, a key point extraction, and a segmentation, of the first
ECG signal to correspond to the second ECG signal of which a
feature vector is to be extracted, and extracts the feature vector
of the first ECG signal using a trained neural network.
Accordingly, the user authentication apparatus 100 determines the
similarity between the first ECG signal and the second ECG signal
based on the features vectors of the first ECG signal and the
second ECG signal.
The user authentication apparatus 100 determines the similarity
between the first ECG signal and the second ECG signal using a
variety of schemes. For example, the user authentication apparatus
100 determines the similarity between the first ECG signal and the
second ECG signal using a cosine distance, a cosine similarity, a
Pearson correlation coefficient, a Euclid distance, a Minkowski
distance, a Mahalanobis distance, and the like.
In operation 330, the user authentication apparatus 100 determines
an authentication threshold corresponding to the reference ECG
signal set 110.
The authentication threshold indicates a threshold of a similarity
used to authenticate the first ECG signal measured from the user.
In an example, the authentication threshold is a preset value or is
determined based on the reference ECG signal set 110.
Referring to FIG. 1, the authentication threshold has a positive
correlation with a number of second reference ECG signals included
in the reference ECG signal set 110 and/or various types of second
reference ECG signals. In one configuration, the authentication
threshold decreases according to a decrease in the number of second
ECG signals included in the reference ECG signal set 110 and/or the
various types of existing second ECG signals. Alternatively, the
authentication threshold increases according to an increase in the
number of second ECG signals included in the reference ECG signal
set 110 and/or the various types of existing second ECG
signals.
Accordingly, the authentication threshold is not a static value and
is a value that dynamically varies in response to updating the
reference ECG signal set 110. Accordingly, the user authentication
apparatus 100 determines again the authentication threshold in
response to updating the reference ECG signal set 110.
In operation 340, the user authentication apparatus 100 determines
whether to authenticate the first ECG signal measured from the user
by comparing the determined similarity and the authentication
threshold.
The user authentication apparatus 100 determines a similarity
between the first ECG signal measured from the user and each of one
or more second ECG signals included in the reference ECG signal set
110. For example, when one or more second ECG signals is included
in the reference ECG signal set 110, the user authentication
apparatus 100 determines the similarity between the first ECG
signal and each of the one or more second ECG signals.
The user authentication apparatus 100 compares a maximum similarity
among one or more similarities to the authentication threshold. For
example, in response to the maximum similarity being less than or
equal to the authentication threshold, the authentication of the
first ECG signal measured from the user fails, that is, the first
ECG signal is unauthenticated. In response to the maximum
similarity being greater than the authentication threshold, the
authentication of the first ECG signal measured from the user
succeeds, that is, the first ECG signal is successfully
authenticated. In addition, the user authentication apparatus 100
determines whether the first ECG signal is successfully
authenticated based on the similarity and the authentication
threshold.
In an example, the authentication threshold is a value that is
determined based on the reference ECG signal set 110, or is a value
that is determined based on a state of the user. For example, the
authentication threshold is a value that is determined based on the
reference ECG signal set 110 including one or more second ECG
signals, or is a value that is determined based on the number
and/or various types of second ECG signals.
As another example, in response to the user being in a stationary
state, the user authentication apparatus 100 determines the
authentication threshold using the second ECG signal, which
indicates a stationary state among second ECG signal included in
the reference ECG signal set 110. That is, the user authentication
apparatus 100 determines the authentication threshold using the
second ECG signal indicating the stationary state, instead of using
the reference ECG signal set 110.
FIG. 4 is a flowchart illustrating an example of a method of
updating a reference ECG signal set using a user authentication
apparatus.
In operation 410, the user authentication apparatus 100 measures a
first ECG signal of a user. For example, in response to a process
or a manipulation that requires a user authentication, the user
authentication apparatus 100 measures an ECG signal of the user to
be authenticated. In an example, the manipulation that requires the
user authentication includes a screen unlock, a payment progress,
and opening of an encrypted file.
In operation 420, the user authentication apparatus 100 determines
a similarity between the first ECG signal of the user and each of
one or more second ECG signals included in the reference ECG signal
set 110.
The user authentication apparatus 100 compares a similarity between
an ECG signal, a feature vector, or an extracted key point of the
first ECG signal and an ECG signal, a feature vector, or an
extracted key point of the second ECG signal included in the
reference ECG signal set 110.
The user authentication apparatus 100 compares a similarity in an
ECG signal, a feature vector, or an extracted key point between the
first ECG signal and the second ECG signal. In an example, the user
authentication apparatus 100 determines the similarity between the
first ECG signal and the second ECG signal using a cosine distance,
a cosine similarity, a Pearson correlation coefficient, a Euclid
distance, a Minkowski distance, a Mahalanobis distance, and other
mathematical procedure.
In operation 430, the user authentication apparatus 100 determines
an authentication threshold corresponding to the reference ECG
signal set 110.
In one example, the authentication threshold is not a static value
and dynamically varies. For example, the user authentication
apparatus 100 determines again the authentication threshold in
response to updating the reference ECG signal set 110. In detail,
the authentication threshold has a positive correlation with
various types and/or a number of second ECG signals.
In one example, the authentication threshold is determined to
correspond to the first ECG signal. For example, in response to the
first ECG signal in a stationary state being measured, the
authentication threshold is determined using the second ECG signal
indicating the stationary state in the reference ECG signal set
110.
In operation 440, the user authentication apparatus 100 determines
whether to authenticate the first ECG signal by comparing the
similarity and the authentication threshold. For example, in
response to a maximum similarity among similarities being greater
than the authentication threshold, the authentication of the user
may succeed, that is, the user may be successfully authenticated.
In response to the maximum similarity being less than or equal to
the authentication threshold, the authentication of the user fails,
that is, the user is unauthenticated.
Operations 410 through 440 of FIG. 4 corresponds to operations 310
through 340 of FIG. 3, and a further description related thereto
may refer to the description made above related to operations 310
through 340.
In one example, a method of performing user authentication using an
ECG signal and updating a reference ECG signal set includes
operations 410 through 460. Hereinafter, update through operations
450 and 460 may be referred to as a first update.
In another example, a method of performing user authentication
using an ECG signal and updating a reference ECG signal set
includes operations 410 through 440 and 470 and 480. Hereinafter,
update through operations 470 and 480 is referred to as a second
update.
In response to the authentication of the first ECG signal failing,
that is, in response to the first ECG signal being unauthenticated,
in operation 450, the user authentication apparatus 100 performs
the user authentication using another authentication method.
Prior to performing the user authentication using the other
authentication method, the user authentication apparatus 100
verifies whether the first update of the reference ECG signal set
110 is performed less than a preset number of times. If the first
update is performed less than the preset number of times, the user
authentication apparatus 100 performs the user authentication using
another authentication method excluding the ECG signal-based user
authentication method.
In an example, the other authentication method excluding the ECG
signal-based user authentication method includes methods of
authenticating the user. For example, the user authentication is
performed using a password, an iris, a fingerprint recognition, and
a pattern recognition.
In response to the user being unauthenticated even using the other
authentication method excluding the ECG signal-based user
authentication method, the user authentication apparatus 100
displays a result as "Authentication failed" instead of performing
the first update on the reference ECG signal set 110.
In response to the user being successfully authenticated using the
other authentication method excluding the ECG signal-based user
authentication method, the user authentication apparatus 100
displays a result as "Authentication succeeded".
In operation 460, in response to the user being successfully
authenticated using the other authentication method excluding the
ECG signal-based user authentication method, the user
authentication apparatus 100 performs the first update on the
reference ECG signal set 110. In an example, a false-rejected ECG
signal is included in the reference ECG signal set 110 through the
first update. The false-rejected ECG signal indicates an ECG signal
in a new user state that is not included in the reference ECG
signal set 110.
If a number of second ECG signals included in the reference ECG
signal set 110 is less than a preset number, the user
authentication apparatus 100 performs or executes the first update
by adding the first ECG signal corresponding to the false-rejected
ECG signal to the reference ECG signal set 110 as the second ECG
signal.
In response to the number of second ECG signals included in the
reference ECG signal set 110 being greater than or equal to the
preset number, the user authentication apparatus 100 calculates a
sum of similarities between the second ECG signals included in the
existing reference ECG signal set 110. The user authentication
apparatus 100 removes the second ECG signal corresponding to a
maximum similarity sum and performs or executes the first update by
adding the false-rejected ECG signal to the reference ECG signal
set 110.
Accordingly, in response to the number of second ECG signals
included in the reference ECG signal set 110 being less than the
preset number, the user authentication apparatus 100 adds the
false-rejected ECG signal to the reference ECG signal set 110. In
response to the number of second ECG signals included in the
reference ECG signal set 110 being greater than or equal to the
preset number, the user authentication apparatus 100 adds the
false-rejected ECG signal instead of using the second ECG signal
corresponding to the maximum similarity sum between the second ECG
signal and one or more other second ECG signals included in the
reference ECG signal set 110.
The user authentication apparatus 100 decreases the similarity
between the second ECG signals in the reference ECG signal set 110,
so that the reference ECG signal set 110 includes second ECG
signals corresponding to further various states.
Accordingly, the user authentication apparatus 100 performs the
first update indicating the new reference ECG signal set by
increasing the number of second ECG signals included in the
reference ECG signal set 110 or by adding the false-rejected ECG
signal instead of using the existing second ECG signal. The
authentication threshold varies based on the new reference ECG
signal set.
In response to the authentication of the first ECG signal
succeeding, that is, in response to the first ECG signal being
successfully authenticated, in operation 470, the user
authentication apparatus 100 determines whether the maximum
similarity determined in operation 420 is greater than an update
threshold.
In an example, in operation 430, the update threshold is greater
than the authentication threshold determined. The update threshold
indicates a threshold used to determine whether to update the
existing reference ECG signal set 110.
In response to the authentication threshold determined in operation
430 being greater than the update threshold of the existing
reference ECG signal set 110, in operation 480, the user
authentication apparatus 100 performs the second update on the
existing reference ECG signal set 110. Alternatively, in response
to the authentication threshold determined in operation 430 being
less than or equal to the update threshold of the existing
reference ECG signal set 110, the user authentication apparatus 100
does not perform the second update on the existing reference ECG
signal set 110. Hereinafter, operation 480 of performing the second
update is described.
A false sample is not included in the reference ECG signal set 110
by determining whether to perform the second update on the existing
reference ECG signal set 110 using the first ECG signal of the
authenticated user. In an example, the false sample represents an
ECG of an unregistered user.
In operation 480, the user authentication apparatus 100 performs
the second update on the existing reference ECG signal set 110
using the first ECG signal of the user measured in operation
410.
In response to the number of second ECG signals included in the
reference ECG signal set 110 being less than the preset number, the
user authentication apparatus 100 performs the second update by
adding the authenticated first ECG signal to the reference ECG
signal set 110 as the second ECG signal.
In response to the number of second ECG signals included in the
reference ECG signal set 110 being greater than or equal to the
preset number, the user authentication apparatus 100 calculates a
similarity between the authenticated first ECG signal and each of
one or more second ECG signals included in the existing reference
ECG signal set 110. The user authentication apparatus 100 couples
the authenticated first ECG signal and the second ECG signal
indicating the maximum similarity.
Accordingly, in response to the number of second ECG signals
included in the reference ECG signal set 110 being less than the
preset number, the user authentication apparatus 100 adds the
authenticated first ECG signal to the reference ECG signal set 110.
In response to the number of second ECG signals included in the
reference ECG signal set 110 being greater than or equal to the
preset number, the user authentication apparatus 100 calculates a
similarity between the authenticated first ECG signal and each
second ECG signal included in the reference ECG signal set 110 and
couples the authenticated first ECG signal and the second ECG
signal indicating the maximum similarity.
Accordingly, the user authentication apparatus 100 performs or
executes the first update indicating a new reference ECG signal set
by increasing the number of second ECG signals included in the
reference ECG signal set 110 or by adding the false-rejected ECG
signal instead of using the existing second ECG signal. The
authentication threshold also varies based on the new reference ECG
signal set.
The user authentication apparatus 100 reflects a minute change in
an ECG signal of the user by correcting the existing reference ECG
signal set through the second update.
In one example, in the second update, coupling of the ECG signal is
performed using a weight. For example, the user authentication
apparatus 100 couples the first ECG signal and the second ECG
signal corresponding to the maximum similarity based on a weight of
the first ECG signal measured from the user and a weight of the
second ECG signal having the maximum similarity with the first ECG
signal in the reference ECG signal set 110 according to Equation 1
and Equation 2. Z=(X*a+Y*b)/(a+b) [Equation 1]
c=(a.sup.2+b.sup.2)/(a+b) [Equation 2]
In these equations, X denotes the authenticated first ECG signal, Y
denotes the second ECG signal having the maximum similarity with
the first ECG signal, a denotes the weight of the first ECG signal,
b denotes the weight of the second ECG signal, Z denotes the
coupled ECG signal, and c denotes a weight of the coupled ECG
signal.
In an example in which the first ECG signal and the second ECG
signal are coupled, the first ECG signal and the second ECG signal
are coupled based on the same form. For example, in response to the
second ECG signal being stored in a form of an ECG signal itself,
the first ECG signal in a form of an ECG signal itself is coupled
with the second ECG signal. Also, in response to the second ECG
signal being stored in a form of a feature vector, the first ECG
signal is coupled with the second ECG signal using a feature
vector. Also, in response to the second ECG signal being stored in
a form of a key point, the first ECG signal is coupled with the
second ECG signal using a key point form.
In one example, the weight of the authenticated first ECG signal
represents or is indicative of a first predicted weight and a
weight of the second ECG signal added to the reference ECG signal
set 110 through the second update represents or is indicative of
the first predicted weight.
Also, a weight of the second ECG signal added to the reference ECG
signal set 110 through the first update represents or is indicative
of a second predicted weight. A weight of the second ECG signal
coupled through the second update is calculated according to
Equation 1 and Equation 2. A weight of the second ECG signal
initially registered by a registered user represents or is
indicative of a third predicted weight.
In an example, the first predicted weight is less than the second
predicted weight or the third predicted weight. The second
predicted weight and the third predicted weight are equal to each
other or differ from each other. Accordingly, the second ECG signal
acquired through the first update has a relatively great weight and
the reference ECG signal set 110 includes an ECG signal in a
different state of the registered user.
FIG. 5 is a flowchart illustrating an example of a method to
determine an authentication threshold using an authentication
threshold model.
Referring to FIGS. 1 and 5, operation 430 performed by the user
authentication apparatus 100 includes operations 510 and 520. In
operation 510, the user authentication apparatus 100 determines an
authentication threshold model corresponding to the existing
reference ECG signal set 110.
In one example, the authentication threshold model includes one or
more models. The authentication threshold model is acquired through
training based on a neural network or a suitable algorithm.
For example, a first authentication threshold model is acquired
through training based on a first training sample. In an example,
in response to the first update being performed a preset number of
times or more, the first training sample includes a feature vector
of the reference ECG signal set 110 or an authentication threshold
corresponding to each feature vector.
As another example, a second authentication threshold model is
acquired through training based on a second training sample. In an
example, in response to the first update being performed less than
the preset number of times, the second training sample includes a
feature vector or an authentication threshold corresponding to each
feature vector corresponding to the number of second ECG signals
included in the reference ECG signal set 110 being greater than or
equal to the preset number.
As another example, a third authentication threshold model is
acquired through training based on a third training sample. In an
example, in response to the first update being performed less than
the preset number of times, the third training sample includes a
feature vector or an authentication threshold corresponding to each
feature vector corresponding to the number of second ECG signals
included in the reference ECG signal set 110 being less than the
preset number.
In an example, the authentication threshold corresponding to each
feature vector indicates an authentication threshold corresponding
to a false rejection rate (FRR) of the reference ECG signal set 110
corresponding to each feature vector being a preset value. For
example, the authentication threshold corresponding to each feature
vector indicates an authentication threshold corresponding to the
FRR of the reference ECG signal set 110 corresponding to each
feature vector being 5%.
In an example, the feature vector of the reference ECG signal set
110 represents a feature vector for various types of second ECG
signals included in the reference ECG signal set 110 and/or the
number of second ECG signals included in the reference ECG signal
set 110. The feature vector of the reference ECG signal set 110
represents at least one of 16 features of the reference ECG signal
set 110 disclosed in the following Table 1.
For example, at least one of 16 features extracted from the
existing reference ECG signal set 110 is represented as the feature
vector of the existing reference ECG signal set 110. For instance,
a number of remaining first updates denotes a remaining number of
times acquired by subtracting a number of performed first updates
from a preset number of times. The second ECG signal of the first
update denotes the second ECG signal acquired through the first
update and the second ECG signal of the second update denotes the
second ECG signal acquired through the second update.
TABLE-US-00001 TABLE 1 Number of first Number of second Number of
remaining Number of second updated second updated second first
updates ECG signals ECG signals ECG signals Maximum similarity
Minimum similarity Average similarity Intermediate between second
between second between second similarity between ECG signals ECG
signals ECG signals second ECG signals Maximum similarity Minimum
similarity Average similarity Intermediate between first between
first between first similarity between updated second updated
second updated second first updated second ECG signals ECG signals
ECG signals ECG signals Maximum similarity Minimum similarity
Average similarity Intermediate between second between second
between second similarity between updated second updated second
updated second second updated ECG signals ECG signals ECG signals
second ECG signals
In operation 520, the user authentication apparatus 100 acquires
the authentication threshold by applying the feature vector of the
existing reference ECG signal set 110 to the authentication
threshold model.
In operation 510, the user authentication apparatus 100 determines
a first authentication threshold model corresponding to the first
update being performed a preset number of times or more, determines
a second authentication threshold model corresponding to the first
update being performed less than the preset number of times and the
number of second ECG signals included in the reference ECG signal
set 110 being greater than or equal to a preset number. Further,
the user authentication apparatus 100 determines a third
authentication threshold model corresponding to the first update
being performed less than the preset number of times and the number
of second ECG signals included in the reference ECG signal set 110
is less than the preset number.
The user authentication apparatus 100 acquires the authentication
threshold by applying the feature vector extracted through the
existing reference ECG signal set 110 to the determined threshold
model.
In one example, the user authentication method and the user
authentication apparatus 100 update a dynamically varying
authentication threshold based on the reference ECG signal set 110,
and automatically update the reference ECG signal set 110. Also,
the user authentication method and the user authentication
apparatus 100 enhance an identification rate of ECG-signal based
user authentication by including an ECG signal in a different state
of a registered user in the reference ECG signal set 110.
FIG. 6 is a diagram illustrating an example of a user
authentication apparatus including a processor and a memory to
perform user authentication.
Referring to FIG. 6, the user authentication apparatus 100 includes
a processor 610 and a memory 620. The memory 620 stores one or more
instructions executable by the processor 610. The processor 610
executes the one or more instructions stored in the memory 620. By
executing the instructions, the processor 610 performs one or more
operations described above with FIGS. 1 through 5. The processor
610 performs a user authentication using an ECG signal in response
to an instruction.
In one example, the processor 610 identifies a first ECG signal
measured from a user to perform user authentication using an ECG
signal of the user. The processor 610 determines a similarity
between the first ECG signal and each of one or more second ECG
signals based on the identified first ECG signal and one or more
second ECG signals included in a reference ECG signal set that is a
comparison target. The processor 610 determines an authentication
threshold corresponding to the reference ECG signal set and
determines whether to authenticate the first ECG signal measured
from the user by comparing the determined similarity and the
authentication threshold.
In one example, the processor 610 of the user authentication
apparatus 100 updates the reference ECG signal set and changes the
second ECG signal included in the reference ECG signal set based on
the update. Accordingly, the authentication threshold dynamically
varies in response to updating the reference ECG signal.
FIG. 7 illustrates an example of a test result acquired by applying
an ECG signal to user authentication.
If FRR=5%, a graph 701 shows that, in response to update not being
performed, an average false acceptance rate (FAR) of the user
authentication apparatus 100 is 6.27%. If FRR=5%, a graph 702 shows
that, in response to first update being performed using a static
authentication threshold instead of a dynamically varying
authentication threshold, the average FAR of the user
authentication apparatus 100 is 2.41%. Alternatively, in response
to FRR=5%, a graph 703 shows that, in response to first update and
second update being performed using a static authentication
threshold instead of the dynamically varying authentication
threshold, the average FAR of the user authentication apparatus 100
is 2.22%.
In one example, in response to FRR=5%, a graph 704 shows that, in
response to first update being performed using a dynamically
varying authentication threshold, the average FAR of the user
authentication apparatus 100 is 1.85%. Also, if FRR=5%, a graph 705
shows that, in response to first update and second update being
performed using a dynamically varying authentication threshold, the
average FAR of the user authentication apparatus 100 is 1.60%.
Accordingly, in the example in which the dynamically varying
authentication threshold is used by performing first update and/or
the second update on the reference ECG signal set, it is possible
to effectively enhance an identification rate of ECG signal-based
user authentication.
The user authentication apparatus, and other apparatuses, units,
modules, devices, and other components described herein are
implemented by hardware components. Examples of hardware components
that may be used to perform the operations described in this
application where appropriate include controllers, sensors,
generators, drivers, memories, comparators, arithmetic logic units,
adders, subtractors, multipliers, dividers, integrators, and any
other electronic components configured to perform the operations
described in this application. In other examples, one or more of
the hardware components that perform the operations described in
this application are implemented by computing hardware, for
example, by one or more processors or computers. A processor or
computer may be implemented by one or more processing elements,
such as an array of logic gates, a controller and an arithmetic
logic unit, a digital signal processor, a microcomputer, a
programmable logic controller, a field-programmable gate array, a
programmable logic array, a microprocessor, or any other device or
combination of devices that is configured to respond to and execute
instructions in a defined manner to achieve a desired result. In
one example, a processor or computer includes, or is connected to,
one or more memories storing instructions or software that are
executed by the processor or computer. Hardware components
implemented by a processor or computer may execute instructions or
software, such as an operating system (OS) and one or more software
applications that run on the OS, to perform the operations
described in this application. The hardware components may also
access, manipulate, process, create, and store data in response to
execution of the instructions or software. For simplicity, the
singular term "processor" or "computer" may be used in the
description of the examples described in this application, but in
other examples multiple processors or computers may be used, or a
processor or computer may include multiple processing elements, or
multiple types of processing elements, or both. For example, a
single hardware component or two or more hardware components may be
implemented by a single processor, or two or more processors, or a
processor and a controller. One or more hardware components may be
implemented by one or more processors, or a processor and a
controller, and one or more other hardware components may be
implemented by one or more other processors, or another processor
and another controller. One or more processors, or a processor and
a controller, may implement a single hardware component, or two or
more hardware components. A hardware component may have any one or
more of different processing configurations, examples of which
include a single processor, independent processors, parallel
processors, single-instruction single-data (SISD) multiprocessing,
single-instruction multiple-data (SIMD) multiprocessing,
multiple-instruction single-data (MISD) multiprocessing, and
multiple-instruction multiple-data (MIMD) multiprocessing.
The methods illustrated in FIGS. 3-5 that perform the operations
described in this application are performed by computing hardware,
for example, by one or more processors or computers, implemented as
described above executing instructions or software to perform the
operations described in this application that are performed by the
methods. For example, a single operation or two or more operations
may be performed by a single processor, or two or more processors,
or a processor and a controller. One or more operations may be
performed by one or more processors, or a processor and a
controller, and one or more other operations may be performed by
one or more other processors, or another processor and another
controller. One or more processors, or a processor and a
controller, may perform a single operation, or two or more
operations.
Instructions or software to control a processor or computer to
implement the hardware components and perform the methods as
described above are written as computer programs, code segments,
instructions or any combination thereof, for individually or
collectively instructing or configuring the processor or computer
to operate as a machine or special-purpose computer to perform the
operations performed by the hardware components and the methods as
described above. In one example, the instructions or software
include machine code that is directly executed by the processor or
computer, such as machine code produced by a compiler. In another
example, the instructions or software include higher-level code
that is executed by the processor or computer using an interpreter.
Programmers of ordinary skill in the art can readily write the
instructions or software based on the block diagrams and the flow
charts illustrated in the drawings and the corresponding
descriptions in the specification, which disclose algorithms for
performing the operations performed by the hardware components and
the methods as described above.
The instructions or software to control a processor or computer to
implement the hardware components and perform the methods as
described above, and any associated data, data files, and data
structures, are recorded, stored, or fixed in or on one or more
non-transitory computer-readable storage media. Examples of a
non-transitory computer-readable storage medium include read-only
memory (ROM), random-access programmable read only memory (PROM),
electrically erasable programmable read-only memory (EEPROM),
random-access memory (RAM), dynamic random access memory (DRAM),
static random access memory (SRAM), flash memory, non-volatile
memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs,
DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs,
BD-REs, blue-ray or optical disk storage, hard disk drive (HDD),
solid state drive (SSD), flash memory, a card type memory such as
multimedia card micro or a card (for example, secure digital (SD)
or extreme digital (XD)), magnetic tapes, floppy disks,
magneto-optical data storage devices, optical data storage devices,
hard disks, solid-state disks, and any other device that is
configured to store the instructions or software and any associated
data, data files, and data structures in a non-transitory manner
and providing the instructions or software and any associated data,
data files, and data structures to a processor or computer so that
the processor or computer can execute the instructions.
While this disclosure includes specific examples, it will be
apparent after an understanding of the disclosure of this
application that various changes in form and details may be made in
these examples without departing from the spirit and scope of the
claims and their equivalents. The examples described herein are to
be considered in a descriptive sense only, and not for purposes of
limitation. Descriptions of features or aspects in each example are
to be considered as being applicable to similar features or aspects
in other examples. Suitable results may be achieved if the
described techniques are performed in a different order, and/or if
components in a described system, architecture, device, or circuit
are coupled in a different manner, and/or replaced or supplemented
by other components or their equivalents. Therefore, the scope of
the disclosure is defined not by the detailed description, but by
the claims and their equivalents, and all variations within the
scope of the claims and their equivalents are to be construed as
being included in the disclosure.
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